Accounting-Aware Evidence Retrieval for Institutional Due Diligence of Tokenized Trade Receivable RWA - Yuanzheng Chen; Sihan Zhou; Emma Lin (Journal of Technology Informatics and Engineering, 2025)
Institutional investors evaluating tokenized real-world asset (RWA) transactions need retrieval systems that can answer short, ambiguous, and legally loaded due-diligence questions with traceable evidence. Trade receivable pools are especially difficult because the same question may require accounting policy, financial metrics, footnote disclosure, legal covenants, insurance language, servicer reporting, or waterfall mechanics. This study implements and evaluates an accounting-aware evidence-retrieval pipeline for tokenized trade receivable RWA due diligence. The main experiment uses the official FinDER benchmark with 5,703 query-evidence-answer triples, 6,121 annotated evidence references, and 5,830 deduplicated evidence passages derived from financial disclosures. The pipeline compares vanilla sparse retrieval, accounting-aware query rewriting, feature reranking, section-aware evidence selection, and calibrated abstention. On the official FinDER evaluation, query rewriting increased Recall@10 from 28.25% to 28.62%, reranking increased Recall@10 to 33.86% and answer-support accuracy to 24.57%, and section-aware evidence selection achieved 34.44% Recall@10, 24.04% nDCG@10, 8.32% EvidencePrecision@3, and 25.23% answer-support accuracy. The accounting-relevant subset, defined as Accounting, Financials, and Footnotes, achieved 37.10% Recall@10 and 26.54% answer-support accuracy. A supplementary stress check using a public receivables purchase agreement and SEC 2026-04 financial statement notes showed that the same retrieval logic can surface schedule, lock-box, GAAP, receivable, and note-disclosure evidence, while also highlighting the need for table extraction and field-level numerical validation. The findings support a narrower deployment claim: accounting-aware RAG can improve evidence discovery and analyst review, but it is not yet suitable for autonomous investment or accounting decision-making